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Evolutionary mechanism of green innovation behavior in construction enterprises: evidence from the construction industry

Xingwei Li (College of Architecture and Urban-Rural Planning, Sichuan Agricultural University, Chengdu, China)
Xiang Liu (College of Architecture and Urban-Rural Planning, Sichuan Agricultural University, Chengdu, China)
Yicheng Huang (College of Architecture and Urban-Rural Planning, Sichuan Agricultural University, Chengdu, China)
Jingru Li (College of Architecture and Urban-Rural Planning, Sichuan Agricultural University, Chengdu, China)
Jinrong He (College of Architecture and Urban-Rural Planning, Sichuan Agricultural University, Chengdu, China)
Jiachi Dai (College of Architecture and Urban-Rural Planning, Sichuan Agricultural University, Chengdu, China)

Engineering, Construction and Architectural Management

ISSN: 0969-9988

Article publication date: 8 September 2022

Issue publication date: 2 January 2024

871

Abstract

Purpose

The green innovation behavior of construction enterprises is the key to reducing the construction industry's carbon emissions and realizing the green transformation of the construction industry. The purpose of this study is to reveal the evolutionary mechanism of green innovation behavior in construction enterprises.

Design/methodology/approach

This study is based on resource-based theory, Porter's hypothesis and signaling theory. First, a measurement model of the green innovation behavior of construction enterprises was constructed from three aspects: environmental regulation, enterprise resources and public opinion through hierarchical analysis. Then, the state values of the measurement model of green innovation behavior of construction enterprises were calculated through the time series data from 2011–2018. Finally, the Markov chain model was used to predict the evolutionary trend of green innovation behavior of construction enterprises, and the accuracy of the prediction effect of the Markov chain model was verified using the time series data of 2019.

Findings

The Markov chain model of green innovation behavior of construction enterprises constructed in this study has high accuracy. This model finds that the transition of the growth state of green innovation behavior in China's construction industry is fluid and predicts the evolution trend of the innovation behavior of construction enterprises. In the future, the green innovation behavior of construction enterprises has a probability of 70.17% to be in a continuous growth state and 40.27% to be in a rapid growth state.

Originality/value

Based on the Markov chain model of green innovation behavior of construction enterprises, this study finds that the transition of the growth state of green innovation behavior of construction enterprises in China has the characteristics of liquidity. In addition, it reveals the development process of the green innovation behavior of construction enterprises from 2011–2018 and predicts the evolution trend of the green innovation behavior of construction enterprises.

Keywords

Acknowledgements

This work was supported by Special Funds of the National Social Science Fund of China (grant number 18VSJ038), the Scientific Research Startup Foundation for Introducing Talents of Sichuan Agricultural University (grant number 2122996022), the Social Science Special Project of Sichuan Agricultural University Disciplinary Construction Dual Support Program (grant number 2021SYYB05), the Undergraduate Training Program for Innovation and Entrepreneurship of Sichuan Agricultural University (grant number 202110626136), and the Sichuan Students’ Platform for Innovation and Entrepreneurship training program (grant number S202110626136).

Citation

Li, X., Liu, X., Huang, Y., Li, J., He, J. and Dai, J. (2024), "Evolutionary mechanism of green innovation behavior in construction enterprises: evidence from the construction industry", Engineering, Construction and Architectural Management, Vol. 31 No. 1, pp. 159-178. https://doi.org/10.1108/ECAM-02-2022-0186

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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